Senior Machine Learning Engineer II - FlightAware (Remote)

• Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience
• Must be authorized to work in the U.S. without sponsorship now or in the future. RTX will not offer sponsorship for this position.
• Strong knowledge of Python
• Experience with deep learning frameworks: Tensorflow/Keras or PyTorch
• Experience with complex data types: time series, image, video, etc.
• Ability to work within an agile development style

Avionics delivers advanced cockpit displays, vision systems, and comprehensive digital solutions for global government, commercial and business aviation customers. We provide connectivity and managed data services to ensure safety and seamless communication for passengers, crews, and militaries, from aircraft and airports to air traffic management. Join us in creating solutions that connect the world, one flight at a time. Start your application today.

Please ensure the role type (defined below) is appropriate for your needs before applying to this role.

As a Machine Learning Engineer on the Aviation Intelligence team, you will be building our next generation of Machine Learning models that will become new Foresight products, improving our existing models, and through this work, improving the operational efficiency of the world's largest airlines and airports..

We're a small team inside a large org, and this role is meant to grow into someone who brings ideas to the table, not just executes on them.

Within Aviation Intelligence, FlightAware's Predict crew builds state-of-the-art machine learning systems that leverage years of historical data and hundreds of millions of examples in order to build models for computing runway landing times, gate arrival times, taxi durations, and more. The results of these models power the FlightAware Foresight product suite, which streams live predictive aviation data to major customers in industry.

Here's why you might like it: the datasets are genuinely very large, so you get to leverage niche technologies for large datasets and get to flex your math/stats skills from time to time. Performance and efficiency aren't nice-to-haves here, they're fun problems. Much of what we work on is open-ended — and "can this even be solved," is a question that one must enjoy trying to answer. Improvements in our products directly improve the status quo for aviation around the world: airlines and airports run on our predictions, so real people catch flights they'd otherwise miss and skip delays they'd otherwise sit through — without ever knowing your work is why. You don't get many shots at impacting an entire mode of transportation. This is one of them.

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